{
 "cells": [
  {
   "cell_type": "raw",
   "id": "05cfcd8e",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "---\n",
    " Copyright 2021 Institute of Advanced Research in Artificial Intelligence (IARAI) GmbH.\n",
    " IARAI licenses this file to You under the Apache License, Version 2.0\n",
    " (the \"License\"); you may not use this file except in compliance with\n",
    " the License. You may obtain a copy of the License at\n",
    "\n",
    " http://www.apache.org/licenses/LICENSE-2.0\n",
    " Unless required by applicable law or agreed to in writing, software\n",
    " distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    " WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    " See the License for the specific language governing permissions and\n",
    " limitations under the License.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a32bb9a",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "# Traffic4cast 2021 Competition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "502b393b",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "![t4c20logo](../t4c20logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a841b6",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "**About:**\n",
    "In this notebook, we explore the distribution of the test slots.\n",
    "\n",
    "**Caveat:**\n",
    "In order to make the module imports work properly set `PYTHONPATH=$PWD` before launching the notebook server from the repo root folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d232018",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.core.display import display\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "from util.check_data_raw_dir import check_raw_data_dir\n",
    "from util.h5_util import load_h5_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a84ddf05",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time module is not an IPython extension.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(60000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 60 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:80% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%load_ext snakeviz\n",
    "%load_ext time\n",
    "%autoreload 2\n",
    "%autosave 60\n",
    "\n",
    "\n",
    "display(HTML(\"<style>.container { width:80% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e73bc411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File structure ../data/raw:  (✓)\n"
     ]
    }
   ],
   "source": [
    "BASE_FOLDER = \"../data/raw\"\n",
    "check_raw_data_dir(BASE_FOLDER, show_missing=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2225fa83",
   "metadata": {},
   "source": [
    "## The data set 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "502d7994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CHICAGO', 'ISTANBUL', 'VIENNA', 'BERLIN', 'NEWYORK', 'MELBOURNE', 'MOSCOW', 'BARCELONA', 'BANGKOK', 'ANTWERP']\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "cities = [re.search(r\".*/([A-Z]+)\", s).group(1) for s in glob.glob(f\"{BASE_FOLDER}/*\")]\n",
    "print(cities)\n",
    "print(len(cities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "51be55ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CHICAGO', 'ISTANBUL', 'BERLIN', 'MELBOURNE']\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "core_challenge_temporal_transfer_cities = [re.search(r\".*/([A-Z]+)/\", s).group(1) for s in glob.glob(f\"{BASE_FOLDER}/**/*test_temporal.h5\", recursive=True)]\n",
    "print(core_challenge_temporal_transfer_cities)\n",
    "print(len(core_challenge_temporal_transfer_cities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eededa16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['VIENNA', 'NEWYORK']\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "extended_challenge_spatiotemporal_transfer_cities = [\n",
    "    re.search(r\".*/([A-Z]+)/\", s).group(1) for s in glob.glob(f\"{BASE_FOLDER}/**/*test_spatiotemporal.h5\", recursive=True)\n",
    "]\n",
    "print(extended_challenge_spatiotemporal_transfer_cities)\n",
    "print(len(extended_challenge_spatiotemporal_transfer_cities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83ff2055",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MOSCOW', 'BARCELONA', 'BANGKOK', 'ANTWERP']\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "full_training_cities = [c for c in cities if c not in core_challenge_temporal_transfer_cities and c not in extended_challenge_spatiotemporal_transfer_cities]\n",
    "print(full_training_cities)\n",
    "print(len(full_training_cities))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a333bcd7",
   "metadata": {},
   "source": [
    "### Week day distribution of test slots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f113407e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_additional_data = {}\n",
    "for city in core_challenge_temporal_transfer_cities + extended_challenge_spatiotemporal_transfer_cities:\n",
    "    test_additional_data[city] = load_h5_file(glob.glob(f\"{BASE_FOLDER}/{city}/*test_additional*.h5\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4ede527",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-dc6a731adbec>:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2304x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = 3\n",
    "cols = 5\n",
    "scale_factor = 8\n",
    "\n",
    "for competition, color, cities_subset in [\n",
    "    (\"temporal\", \"orange\", core_challenge_temporal_transfer_cities),\n",
    "    (\"spatiotemporal\", \"red\", extended_challenge_spatiotemporal_transfer_cities),\n",
    "]:\n",
    "    rows = 1\n",
    "    cols = len(cities_subset)\n",
    "    fig, axs = plt.subplots(rows, cols, figsize=(cols * scale_factor, rows * scale_factor))\n",
    "\n",
    "    for i, city in enumerate(cities_subset):\n",
    "        ax = axs[i]\n",
    "        ax.set_title(f\"Distribution of test slots {competition} {city}\")\n",
    "\n",
    "        test_slots_city = test_additional_data[city][:, 0]\n",
    "\n",
    "        ax.set_title(f\"Distribution of test slots {competition} {city}\\n({len(test_slots_city)} slots)\")\n",
    "        counts, bins = np.histogram(test_slots_city, 28)\n",
    "        ax.hist(bins[:-1], bins, weights=counts, color=color)\n",
    "\n",
    "        ax.set_xlim([0, 6])\n",
    "        ax.set_xlabel(\"day of week\")\n",
    "        ax.set_ylabel(\"num test slots\")\n",
    "        ax.set_xticklabels([\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab8a5f0",
   "metadata": {},
   "source": [
    "### Day time distribution of test slots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "73f3b638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = 3\n",
    "cols = 5\n",
    "scale_factor = 8\n",
    "\n",
    "for competition, color, cities_subset in [\n",
    "    (\"temporal\", \"orange\", core_challenge_temporal_transfer_cities),\n",
    "    (\"spatiotemporal\", \"red\", extended_challenge_spatiotemporal_transfer_cities),\n",
    "]:\n",
    "    rows = 1\n",
    "    cols = len(cities_subset)\n",
    "    fig, axs = plt.subplots(rows, cols, figsize=(cols * scale_factor, rows * scale_factor))\n",
    "\n",
    "    for i, city in enumerate(cities_subset):\n",
    "        ax = axs[i]\n",
    "        ax.set_title(f\"Distribution of test slots {competition} {city}\")\n",
    "\n",
    "        test_slots_city = test_additional_data[city][:, 1]\n",
    "\n",
    "        ax.set_title(f\"Distribution of test slots {competition} {city}\\n({len(test_slots_city)} slots)\")\n",
    "        counts, bins = np.histogram(test_slots_city, 28)\n",
    "        ax.hist(bins[:-1], bins, weights=counts, color=color)\n",
    "\n",
    "        ax.set_xlim([0, 288])\n",
    "        ax.set_xlabel(\"start time [0,..,240)\")\n",
    "        ax.set_ylabel(\"num test slots\")"
   ]
  }
 ],
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